Optimal Transport based Data Augmentation for Heart Disease Diagnosis and Prediction. (arXiv:2202.00567v1 [eess.SP])
(2 min)
In this paper, we focus on a new method of data augmentation to solve the
data imbalance problem within imbalanced ECG datasets to improve the robustness
and accuracy of heart disease detection. By using Optimal Transport, we augment
the ECG disease data from normal ECG beats to balance the data among different
categories. We build a Multi-Feature Transformer (MF-Transformer) as our
classification model, where different features are extracted from both time and
frequency domains to diagnose various heart conditions. Learning from 12-lead
ECG signals, our model is able to distinguish five categories of cardiac
conditions. Our results demonstrate 1) the classification models' ability to
make competitive predictions on five ECG categories; 2) improvements in
accuracy and robustness reflecting the effectiveness of our data augmentation
method.